@inproceedings{plank-agic-2018-distant,
    title = "Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging",
    author = "Plank, Barbara  and
      Agi{\'c}, {\v{Z}}eljko",
    editor = "Riloff, Ellen  and
      Chiang, David  and
      Hockenmaier, Julia  and
      Tsujii, Jun{'}ichi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/D18-1061/",
    doi = "10.18653/v1/D18-1061",
    pages = "614--620",
    abstract = "a cross-lingual neural part-of-speech tagger that learns from disparate sources of distant supervision, and realistically scales to hundreds of low-resource languages. The model exploits annotation projection, instance selection, tag dictionaries, morphological lexicons, and distributed representations, all in a uniform framework. The approach is simple, yet surprisingly effective, resulting in a new state of the art without access to any gold annotated data."
}